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Comparison · Infra & APIs

ggdist vs metR

A side-by-side editorial comparison of ggdist and metR — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:data-visualizationggplot2r-package

ggdist vs metR: at a glance

FeatureggdistmetR
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themesdata-visualization, uncertainty, bayesian-statistics, ggplot2meteorology, ggplot2, r-package, netcdf
Last editorial update31m ago1h ago
WebsiteVisit →Visit →

What is ggdist?

The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.

ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.

Read the full ggdist trajectory →

What is metR?

A meteorology ggplot2 extension where the netCDF reader became the main event

metR supplies meteorological and oceanographic tools for R: contour and streamline geoms, EOF decomposition, wave fitting, and ReadNetCDF() for getting gridded data in. Development has concentrated heavily on that reader. Version 0.18.0 added subsetting by dimension index, so the first or last ten timesteps can be read without knowing how many exist; 0.18.1 moved time parsing to the CFtime package; 0.18.2 added cdo operations through rcdo and reading across multiple files in parallel, and fixed a subsetting bug where nearest-gridpoint matching could return data outside the requested range entirely.

Read the full metR trajectory →

ggdist vs metR: editorial side-by-side

G
ggdist
INFRA · APIS
0.0

The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.

◆ Current state

ggdist supplies ggplot2 with a compositional vocabulary for distributions — slabs, intervals, dotplots and the sub-geometries that combine them. The last three years moved it from a drawing library to an estimation library: bounded density estimation with Sheather-Jones bandwidth became the default, weights propagate through every density, interval and point summary, and blurred dotplots render Monte Carlo standard error as visual fuzz. The 2025 release rounds this out with per-geometry thickness subscales and settable global defaults.

◆ Where it's heading

Two threads run in parallel and keep converging. One is statistical: pluggable density estimators arrived first, then became the default, then gained weights and quantile histograms. The other is compositional: sub-geometries acquired their own guides, then their own scales, so a slab's thickness axis is now a first-class annotated dimension. Cadence has stretched from twice-yearly to roughly annual, with the recent work tightening existing surface rather than opening new.

◆ Prediction

Subguides gained subscales a release later, so the remaining asymmetry is in the sub-geometry system rather than the statistics; expect the next release to continue that fill-in work.

M
metR
INFRA · APIS
0.0

A meteorology ggplot2 extension where the netCDF reader became the main event

◆ Current state

metR supplies meteorological and oceanographic tools for R: contour and streamline geoms, EOF decomposition, wave fitting, and ReadNetCDF() for getting gridded data in. Development has concentrated heavily on that reader. Version 0.18.0 added subsetting by dimension index, so the first or last ten timesteps can be read without knowing how many exist; 0.18.1 moved time parsing to the CFtime package; 0.18.2 added cdo operations through rcdo and reading across multiple files in parallel, and fixed a subsetting bug where nearest-gridpoint matching could return data outside the requested range entirely.

◆ Where it's heading

Two threads run through the releases. The first is tracking ggplot2, absorbing the linewidth aesthetic, the trans to transform rename and guide compatibility as each landed upstream. The second is narrowing scope while deepening the data path: GetSMNData() was made defunct as too specific for a general package, raster and gdal dependencies were removed, and the udunits2 dependency was dropped when it was orphaned, initially replaced by a homebrewed date parser and eventually by CFtime. The result is a package steadily shedding its own code in favour of specialised upstream libraries.

◆ Prediction

Expect further ReadNetCDF() work, since it has received features in four of the last five releases and the rcdo integration opens a large surface of operations to expose.

Alternatives to ggdist and metR

Other Infra & APIs products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either ggdist or metR.

See all ggdist alternatives → · See all metR alternatives →

Recent activity from ggdist and metR

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 7mo agometRSubset notation fix in the netCDF reader
  2. 11mo agometRcdo operations, parallel multi-file reads, and a subsetting correctness fix
  3. 1y agometRnetCDF time parsing handed to the CFtime package
  4. 1y agoggdistPer-geometry thickness subscales and settable defaults
  5. 1y agometRnetCDF subsetting by dimension index
  6. 1y agometRLongitude scales pass the transform argument correctly
  7. 1y agometREOF rotation takes a function, and scope narrows
  8. 2y agoggdistBlurred dotplots draw Monte Carlo error; weights reach every estimator
  9. 2y agoggdistC++ dotplot binning and safer bandwidth fallbacks
  10. 3y agoggdistBounded density becomes the default; existing charts change
  11. 3y agoggdistCategorical distributions, hex layouts, pluggable density estimators
  12. 4y agoggdistComputed variables shared across sub-geometries

Frequently asked questions

What is the difference between ggdist and metR?

Both compete on the same themes — data-visualization, ggplot2, r-package — within Infra & APIs. ggdist and metR are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is ggdist better than metR?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggdist and metR are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.

What are the best alternatives to ggdist?

Top ggdist alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggdist alternatives" section above for the current picks, or visit /alternatives/ggdist for the full list with editorial commentary on each.

What are the best alternatives to metR?

Top metR alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "metR alternatives" section above for the current picks, or visit /alternatives/metr for the full list with editorial commentary on each.